Plant metabolomics and breeding
| hal.structure.identifier | Résistance Induite et Bioprotection des Plantes - EA 4707 [RIBP] | |
| dc.contributor.author | FERNANDEZ, Olivier | |
| hal.structure.identifier | Biometris | |
| dc.contributor.author | MILLET, Emilie | |
| hal.structure.identifier | Génétique Diversité et Ecophysiologie des Céréales [GDEC] | |
| hal.structure.identifier | Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) [GQE-Le Moulon] | |
| dc.contributor.author | RINCENT, Renaud | |
| hal.structure.identifier | Biologie du fruit et pathologie [BFP] | |
| dc.contributor.author | PRIGENT, Sylvain | |
| hal.structure.identifier | Biologie du fruit et pathologie [BFP] | |
| dc.contributor.author | PÉTRIACQ, Pierre | |
| hal.structure.identifier | Biologie du fruit et pathologie [BFP] | |
| dc.contributor.author | GIBON, Yves | |
| dc.contributor.editor | Elsevier | |
| dc.date.issued | 2021 | |
| dc.identifier.isbn | 978-0-12-821688-0 | |
| dc.description.abstractEn | Metabolomics has become essential in the field of phenotyping, providing information halfway between genomes and phenotypes that can be used to predict complex traits. Thus in plants, studies have shown that metabolomics can be used to predict complex traits such as leaf biomass or yield. From a fundamental point of view, these advances can be used to identify new mechanisms involved in plant performance, for example, through identifying and deciphering QTLs involved in the control of the underlying metabolic pathways. From a breeding perspective, “phenomic selection” protocols combining phenotyping under optimal and/or stressing conditions, and metabolomics can be used to construct models that are able to predict traits of interest. Such developments will only be relevant if high throughput and reasonable costs can be achieved, if possible not far from those of genomic selection. The combination of robotics and nontargeted mass spectrometry already allows to process thousands of samples whereas the current capabilities of bioinformatics make it possible to develop predictive models from thousands variables. | |
| dc.language.iso | en | |
| dc.source.title | Plant Metabolomics in full swing | |
| dc.subject | plante | |
| dc.subject.en | Breeding | |
| dc.subject.en | Phenomics | |
| dc.subject.en | Metabolomics | |
| dc.subject.en | Modeling | |
| dc.subject.en | Prediction | |
| dc.title.en | Plant metabolomics and breeding | |
| dc.type | Chapitre d'ouvrage | |
| dc.identifier.doi | 10.1016/bs.abr.2020.09.020 | |
| dc.subject.hal | Sciences du Vivant [q-bio] | |
| dc.subject.hal | Sciences du Vivant [q-bio]/Biologie végétale | |
| bordeaux.volume | 98 | |
| bordeaux.title.proceeding | Plant Metabolomics in full swing | |
| hal.identifier | hal-03150312 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03150312v1 | |
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